Shi Xujie, Wang Denghui, Li Lei, Wang Yang, Ning Rongsheng, Yu Shuili, Gao Naiyun
State Key Laboratory of Pollution Control and Resource Reuse, College of Environmental Science and Engineering, Tongji University, Shanghai, 200092, China.
State Key Laboratory of Pollution Control and Resource Reuse, College of Environmental Science and Engineering, Tongji University, Shanghai, 200092, China; Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai, 200092, China.
Environ Res. 2025 Feb 1;266:120500. doi: 10.1016/j.envres.2024.120500. Epub 2024 Dec 2.
In recent years, the frequency of harmful algal blooms has increased, leading to the release of large quantities of toxins and compounds that cause unpleasant odors and tastes, significantly compromising drinking water quality. Chlorophyll-a (Chl-a) is commonly used as a proxy for algal biomass. However, current methods for measuring Chl-a concentration face challenges in accurately quantifying algae by categories and effectively adapting to natural aquatic environments. This study combined convolutional neural networks (CNNs) and three-dimensional fluorescence data matrices to address these challenges. The algal classification model achieved over 99.5% accuracy in identifying thirteen types of algal samples, with class activation maps showing that the model primarily focused on algal pigment regions. In determining Chl-a concentrations of each algal species in mixed algae solutions (Microcystis aeruginosa, Cyclotella, and Chlorella), the Chl-a models demonstrated Mean Absolute Percentage Errors (MAPEs) ranging from 6.55% to 10.56% in the ultrapure water background, 11.57%-14.12% in the Qingcaosha Reservoir raw water background, and 21.46%-123.37% in the Lake Taihu raw water background. After calibration, the models were significantly improved, achieving MAPEs ranging from 11.86% to 14.18% in the Lake Taihu raw water background. Discrepancies in determination performance indicated that the intensity and locations of characteristic algal pigment fluorescence peaks greatly influenced the Chl-a models' accuracy. This research introduces a novel approach for algal classification and Chl-a concentration determination in water bodies, with significant potential for practical applications.
近年来，有害藻华的发生频率有所增加，导致大量毒素和产生难闻气味与味道的化合物释放出来，严重影响了饮用水质量。叶绿素a（Chl-a）通常被用作藻类生物量的替代指标。然而，目前测量Chl-a浓度的方法在按类别准确量化藻类以及有效适应天然水生环境方面面临挑战。本研究结合卷积神经网络（CNN）和三维荧光数据矩阵来应对这些挑战。藻类分类模型在识别13种藻类样本时准确率超过99.5%，类激活图显示该模型主要聚焦于藻类色素区域。在测定混合藻类溶液（铜绿微囊藻、小环藻和小球藻）中各藻类物种的Chl-a浓度时，Chl-a模型在超纯水背景下的平均绝对百分比误差（MAPE）为6.55%至10.56%，在青草沙水库原水背景下为11.57% - 14.12%，在太湖原水背景下为21.46% - 123.37%。经过校准后，模型有显著改进，在太湖原水背景下的MAPE为11.86%至14.18%。测定性能的差异表明，藻类色素特征荧光峰的强度和位置对Chl-a模型的准确性有很大影响。本研究引入了一种用于水体中藻类分类和Chl-a浓度测定的新方法，具有显著的实际应用潜力。
